Risk of Developing Insulin Resistance in Adult Subjects with Phenylketonuria: Machine Learning Model Reveals an Association with Phenylalanine Concentrations in Dried Blood Spots

Abstract

Phenylketonuria (PKU) is an autosomal recessive inborn error of metabolism where high phenylalanine (Phe) concentrations cause irreversible intellectual disability that can be prevented by newborn screening and early treatment. Evidence suggests that PKU subjects not adherent to treatment could be at risk of insulin resistance (IR). We studied how Phe concentrations (PheCs) relate to IR using machine learning (ML) and derived potential biomarkers. In our cross-sectional study, we analyzed subjects with neonatal diagnoses of PKU, grouped as follows: 10 subjects who adhered to treatment (G1); 14 subjects who suspended treatment (G2); and 24 control subjects (G3). We analyzed plasma biochemical variables, as well as profiles of amino acids and acylcarnitines in dried blood spots (DBSs). Higher PheCs and plasma insulin levels were observed in the G2 group compared to the other groups. Additionally, a positive correlation between the PheCs and homeostatic measurement assessments (HOMA-IRs) was found, as well as a negative correlation between the HOMA-Sensitivity (%) and quantitative insulin sensitivity check index (QUICKI) scores. An ML model was then trained to predict abnormal HOMA-IRs using the panel of metabolites measured from DBSs. Notably, ranking the features’ importance placed PheCs as the second most important feature after BMI for predicting abnormal HOMA-IRs. Our results indicate that low adherence to PKU treatment could affect insulin signaling, decrease glucose utilization, and lead to IR.

Keywords

• inborn error of metabolism
• glucose metabolism
• artificial intelligence
• explanatory machine learning